1.1 Four assays of quality................................................................ 4 1.2 The structure of appearance.................................................... 11 1.3 Intrinsic versus relational........................................................ 13 1.4 Four refutations....................................................................... 17 2. Qualities and their Places................................................................ 25 2.1 The appearance of space......................................................... 25 2.2 Some brain-mind mysteries..................................................... 26 2.3 Spatial qualia........................................................................... 33 2.4 Appearances partitioned.......................................................... 35 2.5 Ties that bind........................................................................... 38 2.6 Feature-placing introduced...................................................... 43 3 Places Phenomenal and Real............................................................ 47 3.1 Space-time regions.................................................................. 47 3.2 Three varieties of visual field.................................................. 50 3.3 Why I am not an array of impressions..................................... 55 3.4 Why I am not an intentional object......................................... 58 3.5 Sensory identification.............................................................. 61 3.6 Some examples of sensory reference....................................... 66

The dynamics model, which is based on L. Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1–3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.

Understanding the design of a device requires both knowledge of the general physical principles that determine its behavior and knowledge of its intended functions. However, the majority of work in model-based reasoning has focused on using either one of these types of knowledge alone. In order to use both types of knowledge in understanding a device design, one must represent the functional knowledge in such a way that it has a clear interpretation in terms of observed behavior. We propose a new formalism, Causal Functional Representation Language (CFRL), for representing device functions with well-defined semantics in terms of behavior. CFRL allows the specification of conditions that a behavior must satisfy, such as occurrence of temporal sequences of events and causal relations among them and the components. We have used CFRL as the basis for a functional verification program, which determines whether a behavior achieves an intended function.

Experiments offer more reliable evidence on causation than observational studies, which is not to gainsay the contribution to knowledge from observation. Experiments should be analyzed as experiments, not as observational studies. A simple comparison of rates might be just the right tool, with little value added by “sophisticated” models. This article discusses current models for causation, as applied to experimental and observational data. The intention-to-treat principle and the effect of treatment on the treated will also be discussed. Flaws in per-protocol and treatment-received estimates will be demonstrated.

rather 'the experimenter knows how to find the answer and she wants to know whether I know how to find it1. The interpretation of the question is determined in part and revealed by

ABSTRACT. I advance a new theory of causal relevance, according to which causal claims convey information about conditional probability functions. This theory is motivated by the problem of disjunctive factors, which haunts existing probabilistic theories of causation. After some introductory remarks, I present in section 3 a sketch of Eells ' (1991) probabilistic theory of causation, which provides the framework for much of the discussion. Section 4 explains how the problem of disjunctive factors arises within this framework. After rejecting three proposed solutions, I offer in section 6 a new approach to causation which avoids the problem. Decision-theoretic considerations also support the new approach. Section 8 develops the consequences of the new theory for causal explanation. The resulting theory of causal explanation incorporates the new insights while respecting important work on scientific explanation by Salmon (1970), Railton (1981), and Humphreys (1989). My conclusions are enumerated in section 9.

This dissertation is the product of a series of significant evolutions of my initial ideas. There are many people who deserve credit for ensuring that these changes were in the right direction. They include Charles H. Anderson,

Paper presented at the Harvard Faculty Seminar on Experiments in Education.

Several different approaches to the conceptual analysis of causation are guided by the idea that a cause is something that makes a difference to its effects. These approaches seek to elucidate the concept of causation by explicating the concept of a difference-maker in terms of better-understood concepts. There is no better

Abstract Humans know how to reason based on cause and effect, but cause and effect is not enough to draw conclusions due to the problem of imperfect information and uncertainty. To resolve these problems, humans reason combining causal models with probabilistic information. The theory that attempts to model both causality and probability is called probabilistic causation, better known as Causal Bayes Nets. In this work we henceforth adopt a logic programming framework and methodology to model our functional description of Causal Bayes Nets, building on its many strengths and advantages to derive a consistent definition of its semantics. ACORDA is a declarative prospective logic programming which simulates human reasoning in multiple steps into the future. ACORDA itself is not equipped to deal with probabilistic theory. On the other hand, P-log is a declarative logic programming language that can be used to reason with probabilistic models. Integrated with P-log, ACORDA becomes ready to deal with uncertain problems that we face on a daily basis. We show how the integration between ACORDA and P-log has been accomplished, and we present cases of daily life examples that ACORDA can help people to reason about. 1